Information processing method, information processing apparatus, and abnormality determination system

ABSTRACT

In an information processing method of an embodiment, a computer is configured to divide observation data into training data and test data, select non-target data from the training data, select a parameter combination from a parameter set corresponding to the non-target data, classify the non-target data as clusters in a feature space of the parameter combination under clustering setting conditions, generate, for each of the clusters, a model trained to output target data included in the training data when the non-target data of the clusters is input, and reselect the parameter combination or change the clustering setting conditions such that a difference between the target data output from the model and the target data of the test data decreases.

CROSS-REFERENCE TO RELATED APPLICATION

This application is based upon and claims the benefit of priority fromJapanese Patent Application No. 2020-042046, filed Mar. 11, 2020, theentire content of which is incorporated herein by reference.

BACKGROUND Field of the Invention

The present invention relates to an information processing method, aninformation processing apparatus, and an abnormality determinationsystem.

Description of Related Art

A method of learning a certain machine learning model using a trainingdata set in which one side of first data and second data in a causalrelationship is input data and the other side is output data is known.With respect to this, a technology for estimating or predicting targetdata from non-target data by learning in advance a machine learningmodel using a training data set in which the certain target data and thenon-target data other than the target data included in flight data of anaircraft are output data and input data, respectively, and using thetrained model is known (refer to Japanese Unexamined Patent Application,First Publication No. 2010-092355 or Japanese Unexamined PatentApplication, First Publication No. 2002-180851, for example).

SUMMARY

However, in the conventional technology, there are cases in whichestimation accuracy or prediction accuracy of target data according tothe model is not insufficient.

One aspect of the present invention devised in view of suchcircumstances provides an information processing method, an informationprocessing apparatus, and an abnormality determination system capable ofimproving estimation accuracy or prediction accuracy of target dataaccording to a model.

An information processing method, an information processing apparatus,and an abnormality determination system according to the presentinvention employ the following configurations.

-   -   (1) A first aspect of the present invention is an information        processing method, using a computer, including: dividing        observation data into a training data set and a test data set;        selecting a plurality of pieces of non-target data other than        certain target data from a plurality of different types of data        included in the training data set divided from the observation        data; selecting a combination of at least three parameters from        a parameter set corresponding to each type of the selected        plurality of pieces of non-target data; classifying the        non-target data in data densities equal to or greater than a        threshold value as clusters and removing the non-target data in        the data densities less than the threshold value in a feature        space having the parameters of the selected combination as        dimensions under certain clustering setting conditions;        generating, for each of the clusters, a model trained to output        the target data included in the training data set when the        non-target data classified as the clusters is input using a        non-linear regression method; inputting the plurality of pieces        of non-target data included in the test data set divided from        the observation data to the model generated for each cluster,        comparing the target data output from the model to which the        plurality of pieces of non-target data have been input with the        target data included in the test data set, and evaluating the        model on the basis of a result of comparison between the pieces        of target data; and selecting a new combination of parameters        from the parameter set or changing the clustering setting        conditions such that a difference between the target data output        from the model and the target data included in the test data set        decreases.    -   (2) In a second aspect of the present invention, in the        information processing method in the first aspect, the        observation data is flight data of an aircraft equipped with a        gas-turbine engine.    -   (3) In a third aspect of the present invention, in the        information processing method in the first or second aspect, the        computer is configured to preferentially select, from the        plurality of pieces of non-target data included in the test data        set, non-target data closer to the non-target data included in        the training data set than other pieces of non-target data, and        input the selected non-target data to the model.    -   (4) In a fourth aspect of the present invention, in the        information processing method in any one of the first to third        aspects, the computer is configured to select the new        combination of parameters from the parameter set, classify the        non-target data in the data densities equal to or greater than        the threshold value as new clusters and removes the non-target        data in the data densities less than the threshold value in a        new feature space having the parameters of the newly selected        combination as dimensions under the clustering setting        conditions, generate, for each of the new clusters, a new model        trained to output the target data included in the training data        set when the non-target data classified as the new clusters is        input, input the plurality of pieces of non-target data included        in the test data set divided from the observation data to the        model newly generated for each cluster, compare the target data        output from the model to which the plurality of pieces of        non-target data have been input with the target data included in        the test data set, and evaluate the newly generated model on the        basis of a result of comparison between the pieces of target        data.    -   (5) In a fifth aspect of the present invention, in the        information processing method in any one of the first to fourth        aspects, the computer is configured to change the clustering        setting conditions, classify the non-target data in the data        densities equal to or greater than the threshold value as the        new clusters and remove the non-target data in the data        densities less than the threshold value in the feature space        having the parameters of the selected combination as dimensions        under the changed clustering setting conditions, generate, for        each of the new clusters, the new model trained to output the        target data included in the training data set when the        non-target data classified as the new clusters is input, and        input the plurality of pieces of non-target data included in the        test data set divided from the observation data to the model        newly generated for each cluster, compare the target data output        from the model to which the plurality of pieces of non-target        data have been input with the target data included in the test        data set, and evaluate the newly generated model on the basis of        a result of comparison between the pieces of target data.    -   (6) In a sixth aspect of the present invention, in the        information processing method in any one of the first to fifth        aspects, the computer is configured to newly select the        parameter combination in which at least one parameter type is        different compared to the previously selected parameter        combination.    -   (7) In a seventh aspect of the present invention, in the        information processing method in any one of the first to sixth        aspects, the computer is configured to newly select the        parameter combination in which a number of parameters is        different compared to the previously selected parameter        combination.    -   (8) In an eighth aspect of the present invention, in the        information processing method in any one of the first to seventh        aspects, the computer is configured to select the new parameter        combination from the parameter set using an optimization method        or changes the clustering setting conditions.    -   (9) In a ninth aspect of the present invention, in the        information processing method in any one of the first to eighth        aspects, the computer is configured to evaluate, as highest, a        model for which a difference between the target data output from        the model and the target data included in the test data set is        smallest, among the plurality of generated models.    -   (10) A tenth aspect of the present invention is an information        processing apparatus including: a divider which is configured to        divide observation data into a training data set and a test data        set; a first selector which is configured to select a plurality        of pieces of non-target data other than certain target data from        a plurality of different types of data included in the training        data set divided from the observation data by the divider; a        second selector which is configured to select a combination of        at least three parameters from a parameter set corresponding to        each type of the plurality of pieces of non-target data selected        by the first selector; a classifier which is configured to        classify the non-target data in data densities equal to or        greater than a threshold value as clusters and remove the        non-target data in the data densities less than the threshold        value in a feature space having the parameters of the        combination selected by the second selector as dimensions under        certain clustering setting conditions; a generator which is        configured to generate, for each of the clusters, a model        trained to output the target data included in the training data        set when the non-target data classified as the clusters by the        classifier is input using a non-linear regression method; and an        evaluator which is configured to input the plurality of pieces        of non-target data included in the test data set divided from        the observation data by the divider to the model generated by        the generator for each cluster, compare the target data output        from the model to which the plurality of pieces of non-target        data have been input with the target data included in the test        data set, and evaluate the model on the basis of a result of        comparison between the pieces of target data, wherein the second        selector is configured to select a new combination of parameters        from the parameter set such that a difference between the target        data output from the model and the target data included in the        test data set decreases, or the classifier is configured to        change the clustering setting conditions such that the        difference between the target data output from the model and the        target data included in the test data set decreases.    -   (11) In an eleventh aspect of the present invention, in the        information processing apparatus in the tenth aspect, the        observation data is flight data of an aircraft equipped with a        gas-turbine engine.    -   (12) In a twelfth aspect of the present invention, in the        information processing apparatus in the tenth or eleventh        aspect, the evaluator is configured to preferably select, from        the plurality of pieces of non-target data included in the test        data set, non-target data closer to the non-target data included        in the training data set than other pieces of non-target data,        and inputs the selected non-target data to the model.    -   (13) A thirteenth aspect of the present invention is an        abnormality determination system including the information        processing apparatus of the eleventh aspect, and an abnormality        determination apparatus, wherein the abnormality determination        apparatus includes: an acquirer which is configured to acquire        flight data of an operation target aircraft equipped with the        gas-turbine engine; a third selector which is configured to        select the plurality of pieces of non-target data from a        plurality of different types of data included in the flight data        acquired by the acquirer; and a determiner which is configured        to input the plurality of pieces of non-target data selected by        the third selector to the model generated by the generator and        determine an abnormality in the operation target aircraft on the        basis of an output result of the model to which the plurality of        pieces of non-target data have been input.    -   (14) In a fourteenth aspect of the present invention, in the        abnormality determination system in the thirteenth aspect, the        determiner is configured to determine that an abnormality has        occurred in the operation target aircraft when the target data        output from the model to which the plurality of pieces of        non-target data have been input is not consistent with the        target data included in the flight data acquired by the        acquirer.    -   (15) In a fifteenth aspect of the present invention, in the        abnormality determination system in the thirteenth or fourteenth        aspect, the abnormality determination apparatus further        includes: a communicator which is configured to communicate with        a terminal device usable by a technician who repairs the        aircraft; and a communication controller which is configured to        transmit a determination result of the determiner to the        terminal device through the communicator.    -   (16) A sixteenth aspect of the present invention is an        abnormality determination apparatus including: an acquirer which        is configured to acquire flight data of an aircraft equipped        with a gas-turbine engine; a selector which is configured to        select a plurality of pieces of non-target data other than        target data from a plurality of different types of data included        in the flight data acquired by the acquirer; and a determiner        which is configured to input the plurality of pieces of        non-target data selected by the selector to a model trained to        output the target data when the plurality of pieces of        non-target data are input and determines an abnormality in the        aircraft on the basis of an output result of the model to which        the plurality of pieces of non-target data have been input.    -   (17) A seventeenth aspect of the present invention is an        abnormality determination method, using a computer, including:        acquiring flight data of an aircraft equipped with a gas-turbine        engine; selecting a plurality of pieces of non-target data other        than target data from a plurality of different types of data        included in the acquired flight data; and inputting the selected        plurality of pieces of non-target data to a model trained to        output the target data when the plurality of pieces of        non-target data are input and determining an abnormality in the        aircraft on the basis of an output result of the model to which        the plurality of pieces of non-target data have been input.    -   (18) An eighteenth aspect of the present invention is a        computer-readable non-transitory storage medium storing a        program for causing a computer to execute: dividing observation        data into a training data set and a test data set; selecting a        plurality of pieces of non-target data other than certain target        data from a plurality of different types of data included in the        training data set divided from the observation data; selecting a        combination of at least three parameters from a parameter set        corresponding to each type of the selected plurality of pieces        of non-target data; classifying the non-target data in data        densities equal to or greater than a threshold value as clusters        and removing the non-target data in the data densities less than        the threshold value in a feature space having the parameters of        the selected combination as dimensions under certain clustering        setting conditions; generating, for each of the clusters, a        model trained to output the target data included in the training        data set when the non-target data classified as the clusters is        input using a non-linear regression method; inputting the        plurality of pieces of non-target data included in the test data        set divided from the observation data to the model generated for        each cluster, comparing the target data output from the model to        which the plurality of pieces of non-target data have been input        with the target data included in the test data set, and        evaluating the model on the basis of a result of comparison        between the pieces of target data; and selecting a new        combination of parameters from the parameter set or changing the        clustering setting conditions such that a difference between the        target data output from the model and the target data included        in the test data set decreases.    -   (19) In a nineteenth aspect of the present invention, in the        storage medium in the eighteenth aspect, the observation data is        flight data of an aircraft equipped with a gas-turbine engine.    -   (20) A twentieth aspect of the present invention is a        computer-readable non-transitory storage medium storing a        program for causing a computer to execute: acquiring flight data        of an aircraft equipped with a gas-turbine engine; selecting a        plurality of pieces of non-target data other than target data        from a plurality of different types of data included in the        acquired flight data; and inputting the selected plurality of        pieces of non-target data to a model trained to output the        target data when the plurality of pieces of non-target data are        input and determining an abnormality in the aircraft on the        basis of an output result of the model to which the plurality of        pieces of non-target data have been input.

According to any aspect of the aforementioned (1) to (20), it ispossible to improve estimation accuracy or prediction accuracy of targetdata according to a model.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating an example of a configuration of anabnormality determination system according to an embodiment.

FIG. 2 is a diagram illustrating an example of a configuration of aninformation processing apparatus according to an embodiment.

FIG. 3 is a flowchart illustrating an example of a flow of a series ofprocesses of the information processing apparatus according to anembodiment (1).

FIG. 4 is a flowchart illustrating an example of a flow of a series ofprocesses of the information processing apparatus according to theembodiment (2).

FIG. 5 is a diagram illustrating an example of flight data.

FIG. 6 is a diagram illustrating an example of a feature space.

FIG. 7 is a diagram schematically illustrating a clustering result inthe future space (a, b, c).

FIG. 8 is a diagram illustrating an example of a clustering result of atraining data set obtained from actual flight data.

FIG. 9 is a diagram schematically illustrating a method of generating aprediction model for each cluster.

FIG. 10 is a diagram illustrating an example of a feature space in whichnon-target data of a test data set is distributed.

FIG. 11 is a diagram illustrating an example of a new feature space.

FIG. 12 is a diagram schematically illustrating a clustering result inthe new feature space (k, l, m).

FIG. 13 is a diagram illustrating an example of a clustering result of atraining data set obtained from actual flight data.

FIG. 14 is a diagram for describing a method of selecting an optimalmodel.

FIG. 15 is a diagram illustrating an example of a configuration of anabnormality determination apparatus according to an embodiment.

FIG. 16 is a flowchart illustrating an example of a flow of a series ofprocesses of the abnormality determination apparatus according to anembodiment.

FIG. 17 is a diagram illustrating an example of a hardware configurationof the information processing apparatus and the abnormalitydetermination apparatus of the embodiment.

DESCRIPTION OF EMBODIMENTS

Hereinafter, embodiments of an information processing method, aninformation processing apparatus, and an abnormality determinationsystem of the present invention will be described with reference to thedrawings. As used throughout this disclosure, the singular forms “a,”“an,” and “the” include plural reference unless the context clearlydictates otherwise.

[Configuration of Abnormality Determination System]

FIG. 1 is a diagram illustrating an example of a configuration of anabnormality determination system 1 according to an embodiment. Asillustrated, the abnormality determination system 1 may include, forexample, an information processing apparatus 100 and an abnormalitydetermination apparatus 200. The abnormality determination apparatus 200is included in an aircraft AC equipped with a gas-turbine engine. Forexample, the aircraft AC may be equipped with a plurality of sensors fordetecting the temperature and the pressure of the gas-turbine engine,the rotation speed of the turbine, and the like and generate flight dataincluding detection values of the sensors. The information processingapparatus 100 and the abnormality determination apparatus 200 areconnected to a network NW. The network NW may be, for example, a widearea network (WAN), a local area network (LAN), or the like.

[Configuration of Information Processing Apparatus]

Hereinafter, a configuration of the information processing apparatus 100will be described. The information processing apparatus 100 may be asingle apparatus or a system in which a plurality of apparatusesconnected via the network NW operate in cooperation. That is, theinformation processing apparatus 100 may be implemented by a pluralityof computers (processors) included in a distributed computing system ora cloud computing system.

FIG. 2 is a diagram illustrating an example of a configuration of theinformation processing apparatus 100 according to an embodiment. Asillustrated, the information processing apparatus 100 may include, forexample, a communicator 102, a controller 110, and a storage 130.

The communicator 102 may include, for example, a wireless communicationmodule including a network interface card (NIC), a receiver, and atransmitter, and the like. The communicator 102 communicates with theabnormality determination apparatus 200, the aircraft AC equipped withthe abnormality determination apparatus 200, and the like through thenetwork NW.

The controller 110 may include, for example, an acquirer 112, a datadivider 114, a data selector 116, a parameter selector 118, a dataclassifier 120, a model generator 122, a model evaluator 124, and acommunication controller 126. The data selector 116 is an example of a“first selector” and the parameter selector 118 is an example of a“second selector.”

The components of the controller 110 may be realized, for example, by aprocessor such as a central processing unit (CPU) or a graphicsprocessing unit (GPU) executing a program stored in the storage 130.Some or all components of the controller 110 may be realized by hardwaresuch as a large scale integration (LSI) circuit, an application specificintegrated circuit (ASIC), or a field-programmable gate array (FPGA), orrealized by software and hardware in cooperation.

The storage 130 may be realized, for example, by a hard disk drive(HDD), a flash memory, an electrically erasable programmable read onlymemory (EEPROM), a read only memory (ROM), a random access memory (RAM),and the like. The storage 130 stores various programs such as firmwareand application programs. The storage 130 stores model data D1 and thelike in addition to programs referred to by a processor. The model dataD1 is information (a program or a data structure) which defines aprediction model MDL which will be described later.

[Processing Flow of Information Processing Apparatus]

Hereinafter, a flow of a series of processes of the informationprocessing apparatus 100 will be described based on flowcharts. FIG. 3and FIG. 4 are flowcharts illustrating an example of a flow of a seriesof processes of the information processing apparatus 100 according to anembodiment. Processes of the present flowcharts may be repeatedlyperformed at a predetermined interval such as 24 hours, one week, or onemonth, for example. In a case where the information processing apparatus100 is implemented by a plurality of computers included in a distributedcomputing system or a cloud computing system, some or all processes ofthe present flowcharts may be processed in parallel by the plurality ofcomputers.

First, the acquirer 112 acquires flight data from the aircraft ACequipped with a gas-turbine engine through the communicator 102 (stepS100). It is assumed that the aircraft AC from which the flight data isacquired is an aircraft having no abnormality in the gas-turbine engine,the airframe, various sensors, and the like thereof. Specifically, theacquirer 112 acquires the flight data from the aircraft AC regarded asan aircraft on which maintenance, repairing or the like has beenperformed a very short period of about several days or several weeks agoand which is unlikely to have an abnormality occurring in thegas-turbine engine, the airframe, or the like thereof. The flight dataof the aircraft AC equipped with the gas-turbine engine is an example of“observation data.”

FIG. 5 is a diagram illustrating an example of flight data. Asillustrated, flight data may be data in which data representingdetection results of various sensors (hereinafter referred to detectiondata) is associated with time. For example, flight data may include aplurality of different types of detection data such as an altitude ALTof the aircraft AC, an entrance temperature T1 of the gas-turbineengine, an exhaust gas temperature EGT of the gas-turbine engine, arotation speed N1 of a low pressure turbine provided in the gas-turbineengine, a rotation speed N2 of a high pressure turbine provided in thegas-turbine engine, a speed CAS of the aircraft AC, and an exit pressureP3 of a compressor provided in the gas-turbine engine. That is, theflight data may be multi-dimensional vector or tensor data.

Description will now return to the flowcharts of FIG. 3 and FIG. 4 .Next, the data divider 114 divides the flight data acquired by theacquirer 112 into a training data set and a test data set (step S102).

Next, the data selector 116 selects a plurality of pieces of non-targetdata other than certain target data from a plurality of pieces ofdetection data included in the training data set divided from the flightdata by the data divider 114 (step S104).

As illustrated in FIG. 5 , in a case where seven types of detection dataare included in the flight data that is a division source, the trainingdata set also includes seven types of detection data. In this case, thedata selector 116 sets a certain type of detection data as target dataand selects the remaining six types of detection data as non-targetdata. For example, target data may be the exit pressure P3 of thecompressor provided in the gas-turbine engine.

Next, the parameter selector 118 selects a combination of at least threeparameters from a parameter set corresponding to each of the pluralityof types of detection data (step S106). For example, in a case where thetraining data set includes the aforementioned seven types of detectiondata, a parameter set includes a total of seven types of parameters(scales or units) such as an altitude, a temperature, a rotation speed,and a speed. In a case where three parameters are selected from such aparameter set and combined, for example, the parameter selector 118selects a single combination from a total of 35 combinations.

Next, the data classifier 120 distributes the plurality of pieces ofnon-target data in a feature space having, as dimensions, the pluralityof parameters included in the parameter combination selected by theparameter selector 118 (step S108). Since the non-target data is a partof the training data set, distribution of the non-target data may bereplaced with distribution of the training data set.

FIG. 6 is a diagram illustrating an example of a feature space. Forexample, it is assumed that the parameter selector 118 selects acombination of three parameters a, b, and c from a parameter set. Inthis case, a feature space becomes a three-dimensional space having theparameters a, b, and c as dimensions. In other words, the feature spacebecomes a space spread by three base vectors when the parameters a, b,and c are used as the base vectors. The feature space may be regarded asa subspace of a vector space corresponding to the training data set. Ina case where four or more parameters are selected as a combination, afeature space becomes a space of four or more dimensions although itcannot be illustrated.

Description will now return to the flowcharts of FIG. 3 and FIG. 4 .Next, the data classifier 120 performs clustering on the plurality ofpieces of non-target data distributed in the feature space under certaindetermined clustering setting conditions (step S110). Since thenon-target data is a part of the training data set, clustering on thenon-target data may be replaced with clustering on the training dataset.

For example, the data classifier 120 may regard the plurality of piecesof non-target data distributed in the feature space as a point set usingthe density-based spatial clustering of applications with noise (DBSCAN)clustering method, classify a plurality of points in a density equal toor greater than a threshold value as one cluster, and remove points in adensity less than the threshold value as outliers (also referred to asnoise values).

More specifically, the data classifier 120 focuses on any one point inthe point set and classifies other points distributed within a certainradius c from the focused point as the same cluster. The radius c is oneof the clustering setting conditions. The data classifier 120 repeatsclassification of points distributed within the radius c as the samecluster while changing focused points. The data classifier 120 excludesa focused point for which other points are not distributed within theradius £, that is, a focused point that is not classified as any clusterfrom the feature space, as an outlier.

FIG. 7 is a diagram schematically illustrating a clustering result infeature data (a, b, c). In the illustrated example, a total of threeclusters C1, C2, and C3 are formed.

FIG. 8 is a diagram illustrating an example of a clustering result of atraining data set obtained from actual flight data. In the illustratedexample, three clusters are also formed in the feature space (a, b, c).

Description will now return to the flowcharts of FIG. 3 and FIG. 4 .Next, the model generator 122 generates a certain regression model foreach cluster formed by the data classifier 120 using a nonlinearregression method (step S112).

For example, when non-target data is input, the model generator 122 maylearn a model such that target data associated with the non-target datathrough time in the training data set is output as a predicted value oran estimated value using support vector regression (SVR) that is anonlinear regression method. For example, it is assumed that non-targetdata Xt₁ and target data Yt₁ are included in a certain piece of trainingdata at a time ti. In this case, the model generator 122 learns a modelsuch that the target data Yt₁ is output when the non-target data Xt₁ isinput. This regression model is referred to as “prediction model MDL” inthe following description.

FIG. 9 is a diagram schematically illustrating a method of generating aprediction model MDL for each cluster. For example, it is assumed thatthree clusters C1, C2, and C3 are formed in the feature space. In thiscase, the model generator 122 generates a dedicated prediction modelMDL1 for the cluster C1 using a plurality of pieces of non-target dataclassified as the cluster C1. Likewise, the model generator 122generates a dedicated prediction model MDL2 for the cluster C2 using aplurality of pieces of non-target data classified as the cluster C2 andgenerates a dedicated prediction model MDL3 for the cluster C3 using aplurality of pieces of non-target data classified as the cluster C3. Inthis manner the prediction model MDL is generated for each cluster.

Description will now return to the flowcharts of FIG. 3 and FIG. 4 .Next, the model evaluator 124 distributes a plurality of pieces ofnon-target data included in the test data set divided by the datadivider 114 from the flight data in a feature space having the parametercombination selected by the parameter selector 118 in the process ofS106 as dimensions (step S114).

Next, the model evaluator 124 preferentially selects non-target dataclose to each cluster from the plurality of pieces of non-target datadistributed in the feature space (step S116). In other words, the modelevaluator 124 preferentially selects non-target data close to non-targetdata included in a training data set classified as each cluster from theplurality of pieces of non-target data of the test data set distributedin the feature space.

FIG. 10 is a diagram illustrating an example of a feature space in whichnon-target data of the test data set is distributed. For example, in acase where the cluster C1 formed using the non-target data of thetraining data set is focused on, non-target data X1 of the test data setis distributed outside the cluster C1 and non-target data X2 isdistributed inside the cluster C1. In this case, the model evaluator 124preferentially selects the non-target data X2 instead of the non-targetdata X1 as non-target data close to the cluster C1. “Non-target dataclose to the cluster C1” is non-target data of the test data set whichis close to non-target data of the training data set classified as thecluster C1. Proximity between pieces of non-target data may becalculated as cosine similarity, for example. In the illustratedexample, non-target data X3 is also distributed in addition to thenon-target data X2 inside the cluster C1. In such a case, the modelevaluator 124 preferentially selects the non-target data X3 closer tothe center of the cluster C1 as non-target data close to the cluster C1.

The same applies to the clusters C2 and C3. For example, non-target dataX4 of the test data set is distributed outside the cluster C2 andnon-target data X5 is distributed inside the cluster C2. In this case,the model evaluator 124 preferentially selects the non-target data X5instead of the non-target data X4 as non-target data close to thecluster C2. “Non-target data close to the cluster C2” is non-target dataof the test data set which is close to non-target data of the trainingdata set classified as the cluster C2.

Non-target data X6 of the test data set is distributed outside thecluster C3 and non-target data X7 is distributed inside the cluster C3.In this case, the model evaluator 124 preferentially selects thenon-target data X7 instead of the non-target data X6 as non-target dataclose to the cluster C3. “Non-target data close to the cluster C3” isnon-target data of the test data set which is close to non-target dataof the training data set classified as the cluster C3.

Description will now return to the flowcharts of FIG. 3 and FIG. 4 .Next, the model evaluator 124 inputs the non-target data preferentiallyselected in the process of S116 to the prediction model MDL of eachcluster (step S118). In the example of FIG. 10 , the model evaluator 124inputs the non-target data X3 to the prediction model MDL1 generated forthe cluster C1, inputs the non-target data X5 to the prediction modelMDL2 generated for the cluster C2, and inputs the non-target data X7 tothe prediction model MDL3 generated for the cluster C3.

Next, the model evaluator 124 acquires target data (hereinafter referredto as predicted target data) that is a predicted value or an estimatedvalue from each prediction model MDL to which the non-target data hasbeen input (step S120).

Next, the model evaluator 124 compares target data associated withnon-target data through time in the test data set with predicted targetdata output from the prediction models MDL and evaluates the accuracy ofthe prediction models MDL on the basis of a comparison result of thetarget data (step S122). For example, the model evaluator 124 maycalculate a difference between target data included in the test data setand predicted target data output from a prediction model MDL andevaluate that the accuracy of the prediction model MDL is higher whenthe difference is smaller.

Next, the model evaluator 124 determines whether the process (=S122) ofevaluating the accuracy of the prediction models MDL has been repeated apredetermined number of times (step S124).

In a case where the model evaluator 124 determines that the accuracyevaluation process is not repeated the predetermined number of times inthe process of S124, the data classifier 120 changes the clusteringsetting conditions using an optimization method (step S126).

The optimization method may be, for example, a combinatorialoptimization method represented by the genetic algorithm. Theoptimization method may be another combinatorial optimization methodsuch as evolution strategy, local search, or the nearest neighboralgorithm instead of or in addition to the genetic algorithm.

For example, the data classifier 120 may change the radius c of DBSCANas the clustering setting conditions.

When the clustering setting conditions have been changed, the dataclassifier 120 distributes a plurality of pieces of non-target data inthe feature space (the same feature space as the previous one) based onthe parameter combination selected in the process of S106. Then, thedata classifier 120 performs clustering on the plurality of pieces ofnon-target data distributed in the feature space under the changedclustering setting conditions. As a result, clusters different fromprevious ones may be formed in the feature space because the clusteringsetting conditions are different from the previous ones although thefeature space is the same as the previous one.

In a case where the model evaluator 124 determines that the accuracyevaluation process is not repeated the predetermined number of times inthe process of S124, the parameter selector 118 may reselect a parametercombination using an optimization method instead of or in addition tothe operation of the data classifier 120 to change the clusteringsetting conditions.

For example, the parameter selector 118 may reselect, from the parameterset, a combination in which at least one parameter type is differentfrom the parameter combination selected in the process of S106. Forexample, when the parameter combination selected in the process of S106is (a, b, c), the parameter selector 118 may reselect a combination inwhich at least any one of the parameters a, b, and c has beensubstituted with another parameter, such as (d, b, c), (a, d, c), or (a,b, d). Further, the parameter selector 118 may reselect a combinationincluding a different number of parameters from the number of parametersincluded in the parameter combination selected in the process of S106.For example, when the parameter combination selected in the process ofS106 is (a, b, c), the parameter selector 118 may increase the number ofcombined parameters, such as (a, b, c, d), (a, b, c, d, e), or (a, b, c,d, e, f).

In a case where the parameter selector 118 reselects a parametercombination, the data classifier 120 distributes a plurality of piecesof non-target data in a new feature space based on the reselectedparameter combination. Then, the data classifier 120 performs clusteringon the plurality of pieces of non-target data distributed in the newfeature space under the same clustering setting conditions as theprevious ones.

FIG. 11 is a diagram illustrating an example of a new feature space. Thefeature space illustrated in FIG. 11 is a three-dimensional space havingparameters k, 1, and m as dimensions. For example, it is assumed thatthe previously selected parameter combination is the parametercombination (a, b, c) illustrated in FIG. 6 . In this case, clustersdifferent from the previous ones may be formed in the feature spacebecause the feature space is different from the previous one althoughthe clustering setting conditions are the same as the previous ones.

FIG. 12 is a diagram schematically illustrating a clustering result inthe new feature space (k, l, m). In the example of FIG. 12 , differentclusters from the clusters illustrated in FIG. 7 are formed.

FIG. 13 is a diagram illustrating an example of a clustering result of atraining data set obtained from actual flight data. In the illustratedexample, the feature space (k, 1, m) is newly formed and differentclusters are formed although the non-target data (the same training dataset) distributed in the feature space is the same as the previous one.

When clusters are newly formed in this manner, the model generator 122regenerates a prediction model MDL for each newly formed cluster using anonlinear regression method. Then, the model evaluator 124 repeats theprocesses of S114 to S124 upon generation of new prediction models MDL.That is, the model evaluator 124 inputs the plurality of pieces ofnon-target data included in the test data set to the prediction modelMDL newly generated for each cluster, compares predicted target dataoutput from the new prediction model MDL with target data included inthe test data set, and evaluates the newly generated prediction modelMDL on the basis of a result of comparison between the pieces of targetdata.

In a case where it is determined that the accuracy evaluation process isrepeated the predetermined number of times in the process of S124, themodel evaluator 124 selects an optimal model from a plurality ofprediction models MDL that have been repeatedly evaluated thepredetermined number of times (step S128).

FIG. 14 is a diagram for describing a method of selecting an optimalmodel. For example, the model evaluator 124 may cause the storage 130 tostore data in which a selected parameter combination, the number ofclusters formed in a feature space, a prediction model MDL correspondingto each cluster, an evaluation result (e.g., a differential value) ofeach prediction model MDL, and a statistics score for evaluation resultsare associated for each accuracy evaluation process in a process ofrepeating the accuracy evaluation process. For example, three clustersmay be formed and three prediction models MDL1, MDL2, and MDL3 may begenerated in the first process. In this case, a score using differentialvalues of the three prediction models MDL1, MDL2, and MDL3 iscalculated. The score may be an arithmetic mean or a weighted mean, forexample. Four clusters may be formed and four prediction models MDL1,MDL2, MDL3, and MDL4 may be generated in the second process. In thiscase, a score using differential values of the four prediction modelsMDL1, MDL2, MDL3, and MDL4 is calculated.

The model evaluator 124 compares such scores and selects a predictionmodel MDL having a lowest score as an optimal model. In the illustratedexample, a combination of the three prediction models MDL1, MDL2, andMDL3 generated in a k-th process has a lowest score. In this case, themodel evaluator 124 selects, as an optimal model, the combination of thethree prediction models MDL1, MDL2, and MDL3 generated when a parametercombination (k, l, m) has been selected.

Next, when the optimal model is selected by the model evaluator 124, thecommunication controller 126 transmits model data D1 defining theoptimal model to the abnormality determination apparatus 200 mounted inthe aircraft AC through the communicator 102 (step S130). For example,in a case where the prediction model MDL is support vector regression,the communication controller 126 may transmit, as the model data D1,information such as a regression coefficient, a slack variable, andkernels. With this, processing of the present flowcharts ends.

[Configuration of Abnormality Determination Apparatus]

Hereinafter, a configuration of the abnormality determination apparatus200 will be described. FIG. 15 is a diagram illustrating an example of aconfiguration of the abnormality determination apparatus 200 accordingto an embodiment. As illustrated, the abnormality determinationapparatus 200 may include, for example, a communicator 202, a display204, a speaker 206, a controller 210, and a storage 230.

The communicator 202 may include, for example, a wireless communicationmodule including an NIC, a receiver, and a transmitter, and the like.The communicator 202 communicates with the information processingapparatus 100 and the like through a network NW.

The display 204 is a user interface that displays various types ofinformation. For example, the display 204 may display an image generatedby the controller 210. The display 204 may display a graphical userinterface (GUI) for receiving various input operations from a user(e.g., a pilot or the like). For example, the display 104 may be aliquid crystal display (LCD), an organic electroluminescence (EL)display, or the like. For example, the display 204 may be provided in acockpit of the aircraft AC, and the like.

The speaker 206 is a user interface that outputs sound. For example, thespeaker 206 may receive an instruction from the controller 210 andoutput sound. For example, the speaker 206 may be provided in thecockpit of the aircraft AC, and the like.

The controller 210 may include, for example, an acquirer 212, a dataselector 214, an abnormality determiner 216, an output controller 218,and a communication controller 220. The data selector 214 is an exampleof a “third selector.”

The components of the controller 210 may be realized, for example, by aprocessor such as a CPU or a GPU executing a program stored in thestorage 230. Some or all components of the controller 210 may berealized by hardware such as an LSI circuit, an ASIC, or an FPGA orrealized by software and hardware in cooperation.

The storage 230 may be realized, for example, by an HDD, a flash memory,an EEPROM, a ROM, RAM, and the like. The storage 230 stores variousprograms such as firmware and application programs. The storage 230stores model data D1 and the like in addition to programs referred to bya processor. The model data D1 may be installed in the storage 230 fromthe information processing apparatus 100 through the network NW, forexample. In a case where a portable storage medium in which the modeldata D1 is stored is connected to a drive device of the abnormalitydetermination apparatus 200, the model data D1 may be installed in thestorage 230 from the portable storage medium.

[Processing Flow of Abnormality Processing Apparatus]

Hereinafter, a flow of a series of processes of the abnormalitydetermination apparatus 200 will be described based on a flowchart. FIG.16 is a flowchart illustrating an example of a flow of a series ofprocesses of the abnormality determination apparatus 200 according to anembodiment. Processes of the present flowchart may be repeatedlyperformed at predetermined intervals, for example. In the followingdescription, the aircraft AC equipped with the abnormality determinationapparatus 200, that is, a host airplane, is referred to as an “operationtarget aircraft AC.”

First, the acquirer 212 acquires flight data from the operation targetaircraft AC (step S200). For example, the acquirer 212 may acquiredetection data from a plurality of sensors attached to a gas-turbineengine and the like of the operation target aircraft AC and regard theplurality of pieces of detection data as flight data. It is assumed thatthis flight data includes the aforementioned target data and non-targetdata.

Next, the data selector 214 selects a plurality of pieces of non-targetdata from the plurality of pieces of detection data included in theflight data acquired by the acquirer 212 (step S202).

Here, the data selector 214 preferentially selects non-target data closeto a cluster corresponding to each prediction model MDL defined by modeldata D1. For example, in a case where a combination of three predictionmodels MDL1, MDL2, and MDL3 generated when a parameter combination (k,l, m) is selected is selected as optimal models, model data D1 is datadefining the prediction models MDL1, MDL2, and MDL3.

In this case, the data selector 214 distributes the plurality of piecesof non-target data included in the flight data acquired by the acquirer212 in a feature space (k, l, m). Then, the data selector 214preferentially selects, from the plurality of pieces of non-target datadistributed in the feature space (k, l, m), non-target data distributedclose to a cluster C1 corresponding to the prediction model MDL1,non-target data distributed close to a cluster C2 corresponding to theprediction model MDL2, and non-target data distributed close to acluster C3 corresponding to the prediction model MDL3. As describedabove, non-target data of the flight data which is close to a cluster isnon-target data of the flight data which is close to non-target data ofa training data set classified as the cluster. In this manner, the dataselector 214 selects non-target data through the same method as thatused when the information processing apparatus 100 evaluates predictionmodels MDL.

Next, the abnormality determiner 216 inputs the non-target data selectedby the data selector 214 to each prediction model MDL defined by themodel data D1 (step S204). For example, it is assumed that the threeprediction models MDL1, MDL2, and MDL3 are optimal models defined by themodel data D1. In this case, the abnormality determiner 216 inputs thenon-target data distributed close to the cluster C1 corresponding to theprediction model MDL1 to the prediction model MDL1, inputs thenon-target data distributed close to the cluster C2 corresponding to theprediction model MDL2 to the prediction model MDL2, and inputs thenon-target data distributed close to the cluster C3 corresponding to theprediction model MDL3 to the prediction model MDL3.

Next, the abnormality determiner 216 acquires predicted target data fromeach prediction model MDL (step S206). Then, the abnormality determiner216 compares predicted target data output from each prediction model MDLwith target data (target data detected by sensors) associated withnon-target data through time in the flight data and determines whetherthe pieces of target data are consistent with each other (step S208).

For example, the abnormality determiner 216 may calculate a differencebetween the target data and the predicted target data for eachprediction model MDL, determine that the pieces of target data areconsistent with each other if the difference is within a permissiblerange and determine that the pieces of target data are not consistentwith each other if the difference does not fall within the permissiblerange.

The abnormality determiner 216 ends processing of the present flowchartwhen the pieces of target data are consistent with each other.

On the other hand, the abnormality determiner 216 determines that anabnormality has occurred in the operation target aircraft AC when thepieces of target data are not consistent with each other (step S210).The “abnormality” may be, for example, at least one of variousabnormalities such as abnormalities in sensors that detect target data,a failure in the gas-turbine engine, and an abnormality in acommunication line that connects a sensor and the abnormalitydetermination apparatus 200.

Next, the output controller 218 causes the display 204 to display, as animage, information representing that an abnormality has occurred in theoperation target aircraft AC (hereinafter referred to as abnormalityreport information) or causes the speaker 206 to output the informationas sound (step S212). Further, the communication controller 220 maytransmit the abnormality report information to a terminal device thatcan be used by a technician who determines whether the operation targetaircraft AC needs to be repaired through the communicator 202. Withthis, processing of the present flowchart ends.

According to the above-described embodiment, the information processingapparatus 100 divides flight data (an example of observation data) ofthe aircraft AC equipped with a gas-turbine engine into a training dataset and a test data set. The information processing apparatus 100selects a plurality of pieces of non-target data other than certaintarget data from a plurality of different types of detection dataincluded in the training data set divided from the flight data. Theinformation processing apparatus 100 selects a combination of at leastthree parameters from a parameter set corresponding to each type of theselected plurality of pieces of non-target data. The informationprocessing apparatus 100 performs clustering in a feature space based onthe parameter combination under certain clustering setting conditions,classifies non-target data in data densities equal to or greater than athreshold value as a cluster, and removes non-target data in datadensities less than the threshold value. The information processingapparatus 100 generates, for each cluster, a prediction model MDLtrained to output target data included in the training data set when aplurality of pieces of non-target data classified as a cluster are inputusing a non-linear regression method represented by support vectorregression. The information processing apparatus 100 inputs a pluralityof pieces of non-target data included in the test data set divided fromthe flight data to the prediction model MDL generated for each cluster,compares predicted target data output from the prediction model MDL withtarget data included in the test data set, and evaluates the predictionmodel MDL on the basis of a result of comparison between the pieces oftarget data. The information processing apparatus 100 selects a newcombination of parameters from parameter sets or changes the clusteringsetting conditions such that a difference between the predicted targetdata output from the prediction model MDL and the target data includedin the test data set decreases. Accordingly, it is possible to improveestimation accuracy or prediction accuracy of target data according tothe prediction model MDL.

Although the information processing apparatus 100 divides the flightdata of the aircraft AC equipped with the gas-turbine engine into thetraining data set and the test data set and generates or evaluates aprediction model MDL using the training data set and the test data setin the above-described embodiment, the present invention is not limitedthereto. For example, the information processing apparatus 100 maydivide various types of data collected during an automated operation ora manual operation of a vehicle or various types of data collected whenan industrial machine, an industrial robot, or the like operates insteadof or in addition to the flight data of the aircraft AC into a trainingdata set and a test data set and generate or evaluate a prediction modelMDL using the training data set and the test data set. Various types ofdata collected during an automated operation or a manual operation of avehicle or various types of data collected when an industrial machine,an industrial robot, or the like operates are other examples of“observation data.”

[Hardware Configuration]

FIG. 17 is a diagram illustrating an example of a hardware configurationof the information processing apparatus 100 and the abnormalitydetermination apparatus 200 of the embodiment. As illustrated, theinformation processing apparatus 100 has a configuration in which acommunication controller 100-1, a CPU 100-2, a RAM 100-3 used as aworking memory, a ROM 100-4 in which a boot program and the like arestored, a storage device 100-5 such as a flash memory and an HDD, adrive device 100-6, and the like are connected through an internal busor a dedicated communication line. The communication controller 100-1communicates with the abnormality determination apparatus 200 and thelike through a network NW. The storage device 100-5 stores a program100-5 a executed by the CPU 100-2. This program is developed in the RAM100-3 according to a direct memory access (DMA) controller (notillustrated) or the like and executed by the CPU 100-2. Accordingly,some or all components of the controller 110 are realized.

The abnormality determination apparatus 200 has a configuration in whicha communication controller 200-1, a CPU 200-2, a RAM 200-3 used as aworking memory, a ROM 200-4 in which a boot program and the like arestored, a storage device 200-5 such as a flash memory and an HDD, adrive device 200-6, and the like are connected through an internal busor a dedicated communication line. The communication controller 200-1communicates with the information processing apparatus 100 and the likethrough a network NW. The storage device 200-5 stores a program 200-5 aexecuted by the CPU 200-2. This program is developed in the RAM 200-3according to a DMA controller (not illustrated) or the like and executedby the CPU 200-2. Accordingly, some or all components of the controller210 are realized.

The above-described embodiment can be represented as follows.

-   -   (1) An information processing apparatus including:    -   at least one memory storing a program; and    -   at least one processor,    -   wherein the processor is configured, by executing the program;    -   to divide flight data of an aircraft equipped with a gas-turbine        engine into a training data set and a test data set;    -   to select a plurality of pieces of non-target data included in        the training data set divided from the flight data and other        than certain target data in a plurality of different types of        data;    -   to select a combination of at least three parameters from a        parameter set corresponding to each type of the selected        plurality of pieces of non-target data;    -   to classify the non-target data in data densities equal to or        greater than a threshold value as clusters and to remove the        non-target data in the data densities less than the threshold        value in a feature space having the parameters of the selected        combination as dimensions under certain clustering setting        conditions;    -   to generate, for each of the clusters, a model trained to output        the target data included in the training data set when the        non-target data classified as the clusters is input using a        non-linear regression method;    -   to input the plurality of pieces of non-target data included in        the test data set divided from the flight data to the model        generated for each cluster, to compare the target data output        from the model to which the plurality of pieces of non-target        data have been input with the target data included in the test        data set, and to evaluate the model on the basis of a result of        comparison between the pieces of target data; and    -   to select a new combination of the parameters from the parameter        set or change the clustering setting conditions such that a        difference between the target data output from the model and the        target data included in the test data set decreases.    -   (2) An abnormality determination apparatus including:    -   an acquirer which is configured to acquire flight data of an        aircraft equipped with a gas-turbine engine;    -   a selector which is configured to select a plurality of pieces        of non-target data other than target data from a plurality of        different types of data included in the flight data acquired by        the acquirer; and    -   a determiner which is configured to input the plurality of        pieces of non-target data selected by the selector to a model        trained to output the target data when the plurality of pieces        of non-target data are input and determine an abnormality in the        aircraft on the basis of an output result of the model to which        the plurality of pieces of non-target data have been input.    -   (3) An abnormality determination method, using a computer,        including:    -   acquiring flight data of an aircraft equipped with a gas-turbine        engine;    -   selecting a plurality of pieces of non-target data other than        target data from a plurality of different types of data included        in the acquired flight data; and    -   inputting the selected plurality of pieces of non-target data to        a model trained to output the target data when the plurality of        pieces of non-target data are input and determining an        abnormality in the aircraft on the basis of an output result of        the model to which the plurality of pieces of non-target data        have been input.    -   (4) A computer-readable non-transitory storage medium storing a        program for causing a computer to execute:    -   dividing observation data into a training data set and a test        data set;    -   selecting a plurality of pieces of non-target data other than        certain target data from a plurality of different types of data        included in the training data set divided from the observation        data;    -   selecting a combination of at least three parameters from a        parameter set corresponding to each type of the selected        plurality of pieces of non-target data;    -   classifying the non-target data in data densities equal to or        greater than a threshold value as clusters and removing the        non-target data in the data densities less than the threshold        value in a feature space having the parameters of the selected        combination as dimensions under certain clustering setting        conditions;    -   generating, for each of the clusters, a model trained to output        the target data included in the training data set when the        non-target data classified as the clusters is input using a        non-linear regression method;    -   inputting the plurality of pieces of non-target data included in        the test data set divided from the observation data to the model        generated for each cluster, comparing the target data output        from the model to which the plurality of pieces of non-target        data have been input with the target data included in the test        data set, and evaluating the model on the basis of a result of        comparison between the pieces of target data; and    -   selecting a new combination of parameters from the parameter set        or changing the clustering setting conditions such that a        difference between the target data output from the model and the        target data included in the test data set decreases.    -   (5) The storage medium according to (4), wherein the observation        data is flight data of an aircraft equipped with a gas-turbine        engine.    -   (6) A computer-readable non-transitory storage medium storing a        program for causing a computer to execute:    -   acquiring flight data of an aircraft equipped with a gas-turbine        engine;    -   selecting a plurality of pieces of non-target data other than        target data from a plurality of different types of data included        in the acquired flight data; and    -   inputting the selected plurality of pieces of non-target data to        a model trained to output the target data when the plurality of        pieces of non-target data are input and determining an        abnormality in the aircraft on the basis of an output result of        the model to which the plurality of pieces of non-target data        have been input.

Although forms for embodying the present invention have been describedusing an embodiment, the present invention is not limited to such anembodiment and various modifications and substitutions can be madewithout departing from the spirit or scope of the present invention.

What is claimed is:
 1. An information processing method, using acomputer, comprising: dividing observation data into a training data setand a test data set; selecting a plurality of pieces of non-target dataother than certain target data from a plurality of different types ofdata included in the training data set divided from the observationdata; selecting a combination of at least three parameters from aparameter set corresponding to each type of the selected plurality ofpieces of non-target data; classifying the non-target data in datadensities equal to or greater than a threshold value as clusters andremoving the non-target data in the data densities less than thethreshold value in a feature space having the parameters of the selectedcombination as dimensions under certain clustering setting conditions;generating, for each of the clusters, a model trained to output thetarget data included in the training data set when the non-target dataclassified as the clusters is input using a non-linear regressionmethod; inputting the plurality of pieces of non-target data included inthe test data set divided from the observation data to the modelgenerated for each cluster, comparing the target data output from themodel to which the plurality of pieces of non-target data have beeninput with the target data included in the test data set, and evaluatingthe model on the basis of a result of comparison between the pieces oftarget data; and selecting a new combination of parameters from theparameter set or changing the clustering setting conditions such that adifference between the target data output from the model and the targetdata included in the test data set decreases.
 2. The informationprocessing method according to claim 1, wherein the observation data isflight data of an aircraft equipped with a gas-turbine engine.
 3. Theinformation processing method according to claim 1, wherein the computeris configured to preferentially select, from the plurality of pieces ofnon-target data included in the test data set, non-target data closer tothe non-target data included in the training data set than other piecesof non-target data, and input the selected non-target data to the model.4. The information processing method according to claim 1, wherein thecomputer is configured to: select the new combination of parameters fromthe parameter set, classify the non-target data in the data densitiesequal to or greater than the threshold value as new clusters and removesthe non-target data in the data densities less than the threshold valuein a new feature space having the parameters of the newly selectedcombination as dimensions under the clustering setting conditions;generate, for each of the new clusters, a new model trained to outputthe target data included in the training data set when the non-targetdata classified as the new clusters is input, and input the plurality ofpieces of non-target data included in the test data set divided from theobservation data to the model newly generated for each cluster, comparesthe target data output from the model to which the plurality of piecesof non-target data have been input with the target data included in thetest data set, and evaluate the newly generated model on the basis of aresult of comparison between the pieces of target data.
 5. Theinformation processing method according to claim 1, wherein the computeris configured to: change the clustering setting conditions, classify thenon-target data in the data densities equal to or greater than thethreshold value as the new clusters and removes the non-target data inthe data densities less than the threshold value in the feature spacehaving the parameters of the selected combination as dimensions underthe changed clustering setting conditions, generate, for each of the newclusters, the new model trained to output the target data included inthe training data set when the non-target data classified as the newclusters is input, and input the plurality of pieces of non-target dataincluded in the test data set divided from the observation data to themodel newly generated for each cluster, compare the target data outputfrom the model to which the plurality of pieces of non-target data havebeen input with the target data included in the test data set, andevaluate the newly generated model on the basis of a result ofcomparison between the pieces of target data.
 6. The informationprocessing method according to claim 1, wherein the computer isconfigured to newly select the parameter combination in which at leastone parameter type is different compared to the previously selectedparameter combination.
 7. The information processing method according toclaim 1, wherein the computer is configured to newly select theparameter combination in which a number of parameters is differentcompared to the previously selected parameter combination.
 8. Theinformation processing method according to claim 1, wherein the computeris configured to select the new parameter combination from the parameterset using an optimization method or changes the clustering settingconditions.
 9. The information processing method according to claim 1,wherein the computer is configured to evaluate, as highest, a model forwhich a difference between the target data output from the model and thetarget data included in the test data set is smallest, among theplurality of generated models.
 10. An information processing apparatuscomprising: a divider which is configured to divide observation datainto a training data set and a test data set; a first selector which isconfigured to select a plurality of pieces of non-target data other thancertain target data from a plurality of different types of data includedin the training data set divided from the observation data by thedivider; a second selector which is configured to select a combinationof at least three parameters from a parameter set corresponding to eachtype of the plurality of pieces of non-target data selected by the firstselector; a classifier which is configured to classify the non-targetdata in data densities equal to or greater than a threshold value asclusters and removes the non-target data in the data densities less thanthe threshold value in a feature space having the parameters of thecombination selected by the second selector as dimensions under certainclustering setting conditions; a generator which is configured togenerate, for each of the clusters, a model trained to output the targetdata included in the training data set when the non-target dataclassified as the clusters by the classifier is input using a non-linearregression method; and an evaluator which is configured to input theplurality of pieces of non-target data included in the test data setdivided from the observation data by the divider to the model generatedby the generator for each cluster, compare the target data output fromthe model to which the plurality of pieces of non-target data have beeninput with the target data included in the test data set, and evaluatethe model on the basis of a result of comparison between the pieces oftarget data, wherein the second selector is configured to select a newcombination of parameters from the parameter set such that a differencebetween the target data output from the model and the target dataincluded in the test data set decreases, or the classifier is configuredto change the clustering setting conditions such that the differencebetween the target data output from the model and the target dataincluded in the test data set decreases.
 11. The information processingapparatus according to claim 10, wherein the observation data is flightdata of an aircraft equipped with a gas-turbine engine.
 12. Theinformation processing apparatus according to claim 10, wherein theevaluator is configured to preferentially select, from the plurality ofpieces of non-target data included in the test data set, non-target datacloser to the non-target data included in the training data set thanother pieces of non-target data, and inputs the selected non-target datato the model.
 13. An abnormality determination system comprising: theinformation processing apparatus according to claim 11; and anabnormality determination apparatus, wherein the abnormalitydetermination apparatus comprises: an acquirer which is configured toacquire flight data of an operation target aircraft equipped with thegas-turbine engine; a third selector which is configured to select theplurality of pieces of non-target data from a plurality of differenttypes of data included in the flight data acquired by the acquirer; anda determiner which is configured to input the plurality of pieces ofnon-target data selected by the third selector to the model generated bythe generator and determine an abnormality in the operation targetaircraft on the basis of an output result of the model to which theplurality of pieces of non-target data have been input.
 14. Theabnormality determination system according to claim 13, wherein thedeterminer is configured to determine that an abnormality has occurredin the operation target aircraft when the target data output from themodel to which the plurality of pieces of non-target data have beeninput is not consistent with the target data included in the flight dataacquired by the acquirer.
 15. The abnormality determination systemaccording to claim 13, wherein the abnormality determination apparatusfurther comprises: a communicator which is configured to communicatewith a terminal device usable by a technician who determines whether theaircraft needs to be repaired; and a communication controller which isconfigured to transmit a determination result of the determiner to theterminal device through the communicator.